// regress function
function Rat43(const B: TVec; X: double): double;
begin
  Rat43 := B[0] / Power((1.0 + Exp(B[1]-B[2]*X)),1/B[3]);
end;

procedure TfrmNonLinTest.FormCreate(Sender: TObject);
var MtxNonLinReg: TMtxNonLinReg;
begin
  MtxNonLinReg := TMtxNonLinReg.Create;
  try
    // Load data - independent variable
    MtxNonLinReg.X.SetIt(false,[9.000, 14.000, 21.000, 28.000,
                                42.000, 57.000, 63.000, 70.000,
                                79.000]);
    // Load data - dependent variable
    MtxNonLinReg.Y.SetIt(false,[8.930, 10.800, 18.590, 22.330,
                                39.350, 56.110, 61.730, 64.620,
                                67.080]);
    // Initial estimates for regression coefficients
    MtxNonLinReg.b.SetIt(false,[100,10,1,1]);
    // setup optimization parameters
    MtxNonLinReg.Tolerance := 1.0e-6; // 6 digits should do the trick
    MtxNonLinReg.GradTolerance := 1.0e-3 // 3 digits
    MtxNonLinReg.MaxIteration := 400;
    MtxNonLinReg.RegressFunction := Rat43; // regression function
    // Marquardt method
    MtxNonLinReg.OptMethod := optMarquardt;
    MtxNonLinReg.Recalc;
    // MtxNinLinReg.b now stores calculated regression parameter estimates
  finally
    MtxNonLinReg.Free;
  end;
end;

#include "MtxExpr.hpp"
#include "StatTools.hpp"
#include "Math387.hpp"

double __fastcall Rat43(TVec* const b,double x)
{
  double* B = b->PValues1D(0);

  return B[0] / Math387::Power((1.0 + Exp(B[1]-B[2]*x)),1.0/B[3]);
}

void __fastcall Example()
{
  TMtxNonLinReg *nlr = new TMtxNonLinReg(NULL);
  try
  {

      const int xLen = 9;
      double xData[xLen] = {9.000, 14.000, 21.000, 28.000, 42.000, 57.000, 63.000, 70.000, 79.000};

      const int yLen = 9;
      double yData[yLen] = {8.930, 10.800, 18.590, 22.330, 39.350, 56.110, 61.730, 64.620, 67.080};

      // Load data - independent variable
      nlr->X->SetIt(false, xData, xLen - 1);

      // Load data - dependent variable
      nlr->Y->SetIt(false, yData, yLen - 1);

      // Initial estimates for regression coefficients
      nlr->B->SetIt(false,OPENARRAY(double,(100,10,1,1)));

      // setup optimization parameters
      nlr->Tolerance = 1.0e-6; // 6 digits should do the trick
      nlr->GradTolerance = 1.0e-3; // 3 digits
      nlr->MaxIteration = 400;
      nlr->RegressFunction = Rat43; // regression function

      // Marquardt method
      nlr->OptMethod = optMarquardt;
      nlr->Recalc();

      // MtxNinLinReg->b now stores calculated regression parameter estimates
  }
  __finally
  {
    nlr->Free();
  }
}